The present invention pertains to speaker recognition system and more particularly to discriminative speaker recognition systems.
Modern methods in digital signal and speech processing have made it possible to assess identity of an individual through audio characteristics of individuals voices. This speaker recognition process makes possible the recognition of each individual from the voice of the person speaking. The end result is the capability to identify the person with a unique identifier or name of the individual.
A typical system for speaker recognition extracts audio features from speech. It then applies a pattern classifier to the features to perform the recognition. The pattern recognition system is either unsupervised or supervised (discriminative).
Previous state of the art methods for discriminative recognition training required large amounts of data transferring and computation for classifying a speaker.
Unsupervised classifiers model the features of an individual person or speaker without reference to features of others. Discriminative pattern classifiers, in contrast, are trained to discriminate between different speakers.
In general, supervised classifiers are more accurate than unsupervised classifiers because they focus on many specific differences between various speakers.
A drawback of supervised classifiers is that they traditionally require large amounts of computation capability to adequately train the processor to recognize a speaker.
Accordingly, it is advantageous to have a means of implementing discriminative speaker recognition that is less complex and less costly than previous methods.